Alert System for Hypo and Hyperglycemia Prevention based on Clinical Risk

ABSTRACT

A device for generating alerts for Hypo and Hyperglycemia Prevention from Continuous Glucose Monitoring (CGM) determines a dynamic risk based on both information of glucose level and a trend obtainable from a CGM signals. The device includes a display whose color depends on the DR (for example, red for high DR, green for low risk). When DR exceeds a certain threshold, alerts are generated to suggest the patient to pay attention to the current glucose reading and to its trend, both of which are shown on the display in numbers and symbols (e.g. an arrow with different slope or color).

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/551,773, filed Oct. 26, 2011, the entirety ofwhich is hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to glucose monitoring systems and, morespecifically, to a method to generate alerts, based on a measure of theclinical risk associated to both glucose level and current trend, forhypoglycemia and hyperglycemia prevention in patients wearing suchdevices.

2. Description of the Related Art

Diabetes is a chronic disease characterized by impaired/absentproduction of a hormone, insulin, which lowers the glucose concentrationin blood after a meal. The standard therapy for diabetes is based oninsulin and drugs administration, diet and physical exercise, tunedaccording to self-monitoring of blood glucose (SMBG) levels 3-4 times aday. Given the inefficiency of SMBG approach in capturing the actualextent of glucose dynamics during the daily life, glycaemia (glucoseconcentration in blood, BG) often exceeds the normality range (70-180mg/dl). Episodes of hypoglycemia (BG<70 mg/dl) and hyperglycemia (BG>180mg/dl) are dangerous for the patient mainly in the short term and in thelong term for respectively. Short-term consequences are dramatic sincethey may lead to diabetic coma, while prolonged periods of hyperglycemiaare associated to the development of complications such as the diabeticretinopathy and nephropathy.

In the last decade, new devices have become available for the continuousmonitoring of the glycaemia. Minimally invasive Continuous GlucoseMonitoring (CGM) devices, such as the Dexcom Seven plus, the MiniMedParadigm Real-Time and Non Invasive Continuous Glucose Monitoring(NI-CGM) are becoming available for clinical practice, providing ameasurement of glucose concentration every 1-5 minutes. Such devices arecrucial, since they provide a real-time and almost continuousinformation on patient's glycemic concentration. In addition, most ofthese devices are provided with an alert generator system, whichgenerates a visual/acoustic alert when hypoglycemic and hyperglycemicthresholds are crossed by the current glycaemia, allowing promptdetection of such threatening events. More important, the almostcontinuous feature of CGM output allows the use of prediction techniquesto anticipate the crossing, and eventually alert the patient of theupcoming event.

Therapeutic actions, such as injection of insulin correction boluses torevert a condition of hyperglycemia- or carbohydrates intake to treathypoglycemia, cannot avoid exposure of the patients to events that canbe threatening, either in the long or short term. In fact, insulinrequires about an hour to be effective and to induce appreciabledecrease in glucose concentration. Also, carbohydrates take time toreach the blood stream in order to compensate the effects ofhypoglycemia. In this framework, generating an alert ahead of the timecould give the patient enough time for the therapeutic actions to beeffective in avoiding the threats of the event itself.

It is important to provide the patient with information about his/herglycemic status in a smart manner, e.g. by supplying information on thecurrent “clinical risk”. Conceptually, the clinical risk is a measure ofthe severity of a specific glycemic condition, which depends mainly onthe glucose level, but might be influenced also by other factors, suchas glucose trend and possibly the quantity of insulin present in thepatient's body. In particular, when evaluating the “clinical risk” towhich the patient is exposed, one should consider not only the glycemiclevel but also its trend. Notably, a device which raises alarms on thebasis of glucose sensor information and critical risk, should exploittechnologies embeddable on a small PDA platform.

CGM Devices and their Alert Systems

Minimally invasive Continuous Glucose Monitoring (CGM) devices currentlyavailable in the market, such as the Dexcom Seven Plus (Dexcom Inc., SanDiego, Calif.), the MiniMed Paradigm Real-Time (Medtronic Inc.,Northridge, Calif.), the Guardian Real-Time (Medtronic Inc., Northridge,Calif.), and the FreeStyle Navigator (Abbott Diabetes Care, Alameda,Calif.), are provided with a visual/acoustic alert generator system thatwarns the patient when hypoglycemic or hyperglycemic thresholds arecrossed. This type of alert is based on the current glycemic valuemeasure by the sensor only. The FreeStyle Navigator or the MiniMedParadigm Real-Time also embeds another alert generator system forhypoglycemia and hyperglycemia, based on the projection of currentglucose level and trend. In particular, the projection method employedin the MiniMed Paradigm Real-Time estimates the current trend using aSavitzky-Golay finite impulse response derivative filter, which ismultiplied by a prediction horizon of 5-30 minutes.

Research on Glucose Prediction Algorithms: State of the Art

The real-time prevention of hypo/hyperglycemic events is a naturalonline application of CGM. As a matter of fact, a few years after theappearance of CGM sensors in the market, some projection methods wereproposed to generate alerts when the actual trend of the glucoseconcentration profile suggested that hypoglycemia was likely to occurwithin a short time. In Choleau et al., for instance, an hypoalert isgenerated when the future glycemic concentration, obtained on the basisof first-order linear extrapolation of the last two/three glucosesamples, is forecasted to cross the hypoglycemic threshold within 20min. Similar methods are implemented in commercial devices, with the aimof delivering alerts for dangerous trends.

Also generation of hypo/hyperalerts can be obtained by means ofahead-of-time prediction of glucose concentration calculated from pastCGM data. Sparacino et al., demonstrated that simple predictionalgorithms based on model with a reduced number of parameters, i.e.either first-order polynomial or first-order auto-regressive (AR(1))models, with time-varying parameters identified by least squares (LS)using a fixed forgetting factor, are suitable for predicting glycaemiaahead in time with a sufficient accuracy, with a PH of 30 and 45 min.Eren-Oruklu et al. developed prediction algorithms based on AR(3) andARMA(3,1) models, with time-varying parameters identified by LS, using aforgetting factor μ which could be modulated according to the glucosetrend. Reifman et al. proposed a predictor based on an AR(10) model,with time-invariant and subject-invariant parameters identified byregularized LS. Similarly, Gani et al. developed a prediction strategybased on an AR(30) model with time-invariant parameters identified byregularized LS on pre-filtered data. Finan et al. proposed a predictorbased on an ARX(3) model with exogenous inputs given by ingestedcarbohydrates and insulin medications, both with time-invariant andtime-variant parameters. Palerm and Bequette, after having posed theproblem in a state-space setting, used the Kalman filtering methodologyto predict glucose level after a given PH, using a double integratedrandom walk as prior for glucose dynamics.

Recently, NN models have been the subject of some investigations forglucose prediction. Pérez-Gandìa et al. developed a feed-forward NN forglucose prediction, trained and tested with 3 different PHs, i.e. 15,30, and 45 min. More recently, Pappada et al. proposed a NN approach topredict glycaemia with a PH of 75 min. Finally, a preliminary studycarried out on a limited dataset consisting of only one patient wasdeveloped by Eskaf et al. Inputs of their NN model include thefirst-order differences of the glycemic time series, and information onmeals, insulin and physical exercise, extracted directly from the bloodglucose time-series, by modeling the glycemic level as a dynamic system.

Margin of Improvements of CGM Devices

Even if several predictive models have been developed to forecast inreal time the future glucose level measured by a CGM device, none of theCGM devices currently available in the market is provided with an alertgeneration system which generates preventive hypoglycemic andhyperglycemic alerts based on the concept of current clinical riskassociated to the glycemic value and its trend.

Clinical Risk Measured by the Dynamic Risk Concept

It has been suggested by Kovatchev and colleagues that the study ofglucose concentration time series should take into account that theglycemic range is asymmetric, with the “hypo range” much narrower thanthe “hyper range” with a much faster increase of health threats whenmoving deeper in the first vs. the latter range. Also, the distributionof glucose concentration values is skewed within the range. In theliterature, transformations of the glucose scale into penalty scoreshave been proposed by Kovatchev, by Hill et al (Glycemic Risk AssessmentDiabetes Equation, GRADE), and by Rodbard (Index of Glycemic Control,ICG). These scores are able to equally weight hypo and hyperglycemicepisodes. As an example of risk score, we consider Kovatchev'sformulation. In this approach, a nonlinear transformation converts everysingle glucose reading into a “static” risk value, which puts moreemphasis on values within the clinically critical regions of hypo andhyperglycemia than in the safe region of normo-glycaemia.

The above mentioned transformations of glucose levels and thecorrespondent indexes are “static”, i.e. a given glycemic level isassociated to a specific penalty or risk score.

Recently a modification of the mathematical definition of riskassociated with glucose levels has been proposed by Guerra et al. inorder to include in the concept of risk not only the actual glycemiclevel, but also the glucose trend. Consider for example a glycemic levelof 65 mg/dl (mild hypoglycemia) with decreasing or increasing trend. Thefirst case (decreasing trend, negative time derivative) refers to a morethreatening condition, since the patient is heading deeper into thehypoglycemic region while in the second case (increasing trend, positivetime derivative) the patient is recovering towards the normo-glycaemia.The new risk function, called the Dynamic Risk (DR) includes thisinformation, assigning higher risk to situation in which the trend isleading to a dangerous zone. In particular the risk is increased whenglucose concentration is close to or in the hypoglycemic range withdecreasing trend and is close to or in the hyperglycemic range withincreasing trend. It has been proved that the DR as formulated isintrinsically predictive, since it allows for alert generation about 10minutes before the actual threshold crossing. Several mathematicalformulations/structures of DR can be implemented exploiting variants ofthose proposed.

SUMMARY OF THE INVENTION

The disadvantages of the prior art are overcome by the present inventionwhich, in one aspect, is a system able to measure the glycaemia in analmost continuous manner (one measurement every 1-10 minutes), whichemploys an algorithm to generate preventive hypoglycemic andhyperglycemic alerts based on the Dynamic Risk concept associated toboth current glucose level and trend. The system alerts the patient witha sensorial alarm that can be tactile, visual or auditory. In apreferred embodiment the system will have a color monitor with a coloredblinking/pulsing back-light option which will be activated when an alarmis raised.

In one aspect, the invention is a system for alerting a patient ofhypoglycemia and hyperglycemia risk that includes a continuous glucosemonitoring (CGM) device configured to determine periodically a glucoselevel in the patient, thereby generating a series of glucose levels. Adynamic risk estimation module is configured to: evaluate a differentialchange in glucose level over time (dg/dt) based on the series of glucoselevels; generate a smoothed glucose level that is indicative of theseries of glucose levels; calculate dg/dt based on the series ofsmoothed glucose levels; and estimate a dynamic risk based on thesmoothed CGM and estimated dg/dt. A comparison circuit compares thedynamic risk to a predetermined threshold. A device monitor that isconfigured to generate a display representative of the smoothed CGM andthat is also configured to generate a perceptible alarm when the dynamicrisk is greater than the predetermined threshold.

Additionally, a circuit calculates an angle α that is a function ofdg/dt and the device monitor is also displays an arrow that is angledfrom horizontal by the angle α. The perceptible alarm could be, forexample, a blinking display, a brightly colored display, a vibratoryalarm, an audible alarm, or any combination of these alarms. The displaymay also show different smoothed CGM values as corresponding differentcolors. The display may also display a trend box that indicates a CGMdata trend.

In another aspect, the invention is a method of monitoring glucose in apatient, in which a periodic series of glucose levels in the patient isreceived from a continuous glucose monitoring (CGM) device. Adifferential change in glucose level is evaluated over time (dg/dt)based on the series of glucose levels. A smoothed glucose level that isindicative of the series of glucose levels is generated. The series ofglucose levels is used to calculate dg/dt. A dynamic risk based on thesmoothed CGM and dg/dt is estimated. The dynamic risk is compared to apredetermined threshold. A display representative of the smoothed CGM isgenerated. A perceptible alarm is generated when the dynamic risk isgreater than the predetermined threshold.

These and other aspects of the invention will become apparent from thefollowing description of the preferred embodiments taken in conjunctionwith the following drawings. As would be obvious to one skilled in theart, many variations and modifications of the invention (includingvariations of mathematical structures and parameters in DR definition)may be effected without departing from the spirit and scope of the novelconcepts of the disclosure.

BRIEF DESCRIPTION OF THE FIGURES OF THE DRAWINGS

FIG. 1 is a diagram showing a dynamic risk space: example of colors thatcan be associated to different glucose levels (x axis) and first timederivative value (y axis).

FIG. 2 is a diagram showing a display in an alert mode. In case of alert(threshold crossing of predicted profile or of DR associated to actualglucose trend and level), the display will be colored with the actual DRcolor code and blink.

FIG. 3 is a diagram showing several examples of different clinicalconditions detected by the continuous glucose monitoring device. [Left]Euglycaemia with almost stable trend: the trend/DR square shows aslightly decreasing arrow on green background. [Center] Hypoglycemiawith decreasing trend: the trend/DR square shows a decreasing arrow onred-blinking background. [Right] Hypoglycemia with increasing trend: thetrend/DR square shows an increasing arrow on orange background. Thecolor is picked from a color map similar to that on FIG. 1, whichassigns a risk color to each pair (glucose-trend).

FIG. 4 is a diagram demonstrating a working principle of the trend/DRsquare. The arrow angle from baseline is a function of the first timederivative. Highest slope (±90° from baseline should be associated toderivatives of ±5 mg/dl or higher; flat arrow should be displayed instable conditions). The background color of the square should be pickedfrom a color map based on DR: an example is shown in FIG. 1 and reportedhere for simplicity.

FIG. 5 is a Flow-chart explaining the algorithm driving the displayactions.

DETAILED DESCRIPTION OF THE INVENTION

A preferred embodiment of the invention is now described in detail.Referring to the drawings, like numbers indicate like parts throughoutthe views. Unless otherwise specifically indicated in the disclosurethat follows, the drawings are not necessarily drawn to scale. As usedin the description herein and throughout the claims, the following termstake the meanings explicitly associated herein, unless the contextclearly dictates otherwise: the meaning of “a,” “an,” and “the” includesplural reference, the meaning of “in” includes “in” and “on.”

A device for the continuous monitoring of glycaemia, either minimally ornoninvasive, will be employed. An algorithm for the evaluation of theclinical risk measure called Dynamic Risk (DR) will be embedded in thedevice and will drive the activation of the alerts.

The embodiment of the invention here proposed includes in a DRevaluation method, which comprises both a static component able toequally weight hypo and hyperglycemic events, and a component which isable to account for the trend of the signal. The algorithm predictsthreshold crossings, i.e. it detects and predicts approaches to riskyzones of hypo/hyperglycemia with a significant temporal gain (e.g., of10 or more minutes). One embodiment of the device includes a monitorwhere the glycemic level is shown along with a trend arrow, which canchange its slope and color according to the estimated clinical risk. Inone preferred setting the display background can be highlighted withdifferent colors and can blink when threatening conditions areapproaching.

Estimation of the Dynamic Risk in Real Time

The algorithm for the assessment of the clinical risk by the computationof the Dynamic Risk receives as an input:

-   -   The glycemic level measured by the CGM device    -   The estimate of the first time derivative    -   Parameters defining the relative importance of risk associated        to glucose level and glucose trend.

The dynamic risk (DR) should preferably respect following equations:

$\left\{ \begin{matrix}{{{DR}} > {r(g)}} & {{{if}\mspace{14mu} {\frac{g}{t} \cdot {{SR}(g)}}} > 0} \\{{{DR}} < {r(g)}} & {{{if}\mspace{14mu} {\frac{g}{t} \cdot {{SR}(g)}}} < 0}\end{matrix} \right.$

Where r(g) is a score function that maps glucose levels in risk scoresand dg/dt is the differential change in glucose level over thedifferential change in time, estimated possibly by means of aregularized deconvolution algorithm. As example Kovatchev's riskfunction can be used: r(g)=10·f(g)² with f(g)=γ·[(ln(g))^(α)−β]; α, β, γbeing scalars equal to 1.084, 5.381 and 1.509 (assuming glucoseexpressed in mg/dl). The above function r(g) maps the glycemic range[20-600] mg/dl to the (static) risk space range [0-100][0÷100]. Thismeans that given a specific risk score for glycemic levels per se, adynamic risk evaluating also the risk associated to the trend willassign higher risk if the trend is leading to threatening zones. Inparticular, hypoglycemia with decreasing trend and hyperglycemia withincreasing trend will be assigned with highest risk. If other DRfunctions are employed with these characteristics, they shouldpreferably be continuous in the working range.

In one representative embodiment, the Dynamic Risk can be defined as in:

${{DR}\left( {g,\frac{g}{t}} \right)} = \left\{ {{\begin{matrix}{{{SR}(g)} \cdot ^{{+ \mu}\frac{r}{t}}} & {{{if}\mspace{14mu} {{SR}(g)}} > 0} \\{{{SR}(g)} \cdot ^{{- \mu}\frac{r}{t}}} & {{{if}\mspace{14mu} {{SR}(g)}} < 0}\end{matrix}{where}{{SR}(g)}} = {{{r_{h}(g)} - {{r_{l}(g)}{r_{l}(g)}}} = \left\{ {{\begin{matrix}{r(g)} & {{{if}\mspace{14mu} {f(g)}} < 0} \\0 & {otherwise}\end{matrix}{r_{h}(g)}} = \left\{ \begin{matrix}{r(g)} & {{{if}\mspace{14mu} {f(g)}} > 0} \\0 & {otherwise}\end{matrix} \right.} \right.}} \right.$

Other structures of DR may be employed, for instance otherimplementations can be used which is based on the hyperbolic orarctangent:

${{DR}_{\tanh}\left( {g,\frac{g}{t}} \right)} = \left\{ {{\begin{matrix}{{{SR}(g)} \cdot \left\lbrack {{\delta \cdot {\tanh \left( {{\alpha \frac{r}{t}} + \gamma} \right)}} + \beta} \right\rbrack} & {{{if}\mspace{14mu} {{SR}(g)}} > 0} \\{{{SR}(g)} \cdot \left\lbrack {{\delta \cdot {\tanh \left( {{{- \alpha}\frac{r}{t}} + \gamma} \right)}} + \beta} \right\rbrack} & {{{if}\mspace{14mu} {{SR}(g)}} < 0}\end{matrix}{{DR}_{atan}\left( {g,\frac{g}{t}} \right)}} = \left\{ \begin{matrix}{{{SR}(g)} \cdot \left\lbrack {{{\delta \cdot a}\; {\tan \left( {{\alpha \frac{r}{t}} + \gamma} \right)}} + \beta} \right\rbrack} & {{{if}\mspace{14mu} {{SR}(g)}} > 0} \\{{{SR}(g)} \cdot \left\lbrack {{{\delta \cdot a}\; {\tan \left( {{{- \alpha}\frac{r}{t}} + \gamma} \right)}} + \beta} \right\rbrack} & {{{if}\mspace{14mu} {{SR}(g)}} < 0}\end{matrix} \right.} \right.$

An important issue to be address is how the first time derivative iscomputed in the device, since measurement noise can heavily affect thequality of the estimation of the first derivative signal. If the signalto noise ratio (SNR) is sufficiently high, i.e. the noise has lowamplitude with respect to the glucose signal, one can evaluate thederivative as first order finite differences. If the SNR is low and thenoise component is significant, a deconvolution based approach for thesimultaneous estimation of the first time derivative and of a smoothedversion of the CGM signal is used. The method should be implemented in apreferred embodiment.

As stated above, the DR is intrinsically predictive, since it amplifiesthe glycaemia in the risk space whenever the glycaemia itself isapproaching a clinically critical region. In a preferred embodiment theDR can be exploited for its predictive features as follows:

-   -   As standalone predictive tool: evaluate the DR of the glycemic        level as it is measured by the (NI)-CGM.    -   To evaluate the clinical risk of a predictive profile: evaluate        the DR of a predicted glycemic profile. For example we propose        an embodiment where a short-term prediction of the glycemic        profile is obtained via Kalman Filter, and then translated into        the DR space. In this way one can sum up the temporal gain        obtained via simple prediction with the localized amplification        where the predicted glycaemia is heading towards a hypo/hyper        region.    -   To modulate on the basis of the DR of the glucose concentration        the visual/acoustic level of the hypoglycemic and hyperglycemic        alarms generated by either the CGM measured value or the        predicted CGM value.        -   To determine when the visual/acoustic alert status should be            stopped (e.g. when the CGM profile is passing from the            hypoglycemic to the euglycemic range with increasing trend,            or from the hyperglycemic to the euglycemic range with            decreasing trend).

As a signal to be fed to a prediction algorithm: perform a prediction ofthe DR profile with literature prediction algorithms, e.g. theautoregressive model of order one presented in Sparacino et al. (IEEETrans Biomed Eng 2007) or the neural network presented in Zecchin et al.(IEEE Trans Biomed Eng 2012). The system should raise an alarm wheneverthe predicted profile, Clinical DR, or Clinical DR of the predictedglycemic profile exceeds a threshold, which could be fixed in the CGMdevice, or settable by the patient, or individualized on from user touser. The alarm can be given in form of sound, voice, vibration,constant/blinking/pulsing light placed on the devices or on its monitor,or in any other way that is usually employed by commercial devices.

Output for the Patient and Display

In the system proposed in this invention, a colored monitor 100 is usedas an alert system as explained below. In fact, specific combination ofglucose level and trend can be associated to risk color accordingly withDR. For example, a scale of colors can be used considering the followinggraph (as shown in FIG. 1) (Please note that the different colors arerepresented in FIG. 1 as different shades of gray.):

In a device monitor 200, the background of the display can be coloredaccordingly with DR as shown in FIG. 2. In particular, when a threshold,which can be fixed, settable by the user or individualized on thespecific user, is crossed and an alert needs to be raised, thebackground of the screen can pulse in red, or in any appropriate color,warning the patient that he/she is reaching a risky condition.

In another implementation, a colored squared area can be dedicated tothe display of an arrow 300 indicating the trend. In a preferredembodiment, the square should blink when the patient is approachingrisky regions. An example of such situations is shown in FIG. 3. Thecolor of a trend box 302 could be a function of DR, with higher DR inabsolute value are associated to red color, while lower risks areassociated to orange yellow and finally green for safe conditions. Thearrow 300 displayed in the trend box 302 represents the trend evaluatedvia a smart algorithm (e.g. via deconvolution via finite differences).The angle from the baseline is a function of the first time derivativeof the measured signal.

In one embodiment, as shown in FIG. 4, the angle (α) of the arrow 300relative to the horizontal can be proportional to dg/dt. Also, otherimplementation could comprise a LED light on the device which isactivated whenever a risky situation is near.

As shown in FIG. 5, one representative embodiment of an alert system 500includes a device monitor 200 (as shown in FIGS. 1-4) that includes aCGM signal box display showing data 520 (such as the display shown inFIG. 1) from a continuous glucose monitoring (CGM) device input and atrend box 302. A color map 518 is used to map CGM signals to the CGMsignal box 520. The device monitor can include a dynamic risk estimationmodule 504 (which can be embodied as a digital circuit running a programstored in a digital memory) programmed to receive a new continuousglucose monitoring value 502 from the continuous glucose monitoringdevice and evaluate the differential change in glucose level over time(dg/dt) 506, generate a smoothed CGM signal 508, which is displayed inthe CGM signal box 520. The DR estimation module 504 also calculates thedg/dt to compute 516 the angle α of the arrow in the trend box 302. TheDR estimation module 504 then evaluates the dynamic risk (DR) 512 basedon the CGM value and dg/dt. If the DR is greater than a threshold 514,then the system generates a blinking display, a brightly coloreddisplay, a vibratory alarm, an audible alarm, or a combination of thesealarms.

The above described embodiments, while including the preferredembodiment and the best mode of the invention known to the inventor atthe time of filing, are given as illustrative examples only. It will bereadily appreciated that many deviations may be made from the specificembodiments disclosed in this specification without departing from thespirit and scope of the invention. Accordingly, the scope of theinvention is to be determined by the claims below rather than beinglimited to the specifically described embodiments above.

What is claimed is:
 1. A system for alerting a patient of hypoglycemiaand hyperglycemia risk, comprising: (a) a continuous glucose monitoring(CGM) device configured to determine periodically a glucose level in thepatient, thereby generating a series of glucose levels; (b) a dynamicrisk estimation module that is configured to: (i) evaluate adifferential change in glucose level over time (dg/dt) based on theseries of glucose levels; (ii) generate a smoothed glucose level that isindicative of the series of glucose levels; and (iii) estimate a dynamicrisk based on the smoothed CGM and dg/dt; (c) a comparison circuit thatcompares the dynamic risk to a predetermined threshold; and (d) a devicemonitor that is configured to generate a display representative of thesmoothed CGM and that is also configured to generate a perceptible alarmwhen the dynamic risk is greater than the predetermined threshold. 2.The system of claim 1, wherein the system evaluates a differentialchange in glucose level over time (dg/dt) based on the series of glucoselevels by employing a regularized deconvolution algorithm.
 3. The systemof claim 1, further comprising a circuit that is configured to calculatean angle α that is a function of dg/dt and wherein the device monitor isalso configured to display an arrow that is angled from horizontal bythe angle α.
 4. The system of claim 1, wherein the perceptible alarmcomprises an alarm selected from a list consisting of: a blinkingdisplay, a brightly colored display, a vibratory alarm, an audiblealarm, and combinations thereof.
 5. The system of claim 1, wherein thedisplay is configured to show different smoothed CGM values ascorresponding different colors.
 6. The system of claim 1, wherein thedisplay is further configured to display a trend box that indicates aCGM data trend.
 7. A method of monitoring glucose in a patient,comprising the steps of: (a) receiving from a continuous glucosemonitoring (CGM) device a periodic series of glucose levels in thepatient; (b) evaluating a differential change in glucose level over time(dg/dt) based on the series of glucose levels; (c) generating a smoothedglucose level that is indicative of the series of glucose levels; (d)estimating a dynamic risk based on the smoothed CGM and dg/dt; (e)comparing the dynamic risk to a predetermined threshold; and (f)generating a display representative of the smoothed CGM and generating aperceptible alarm when the dynamic risk is greater than thepredetermined threshold.
 8. The method of claim 7, further comprisingthe step of displaying an arrow, such as a colored arrow, indicative ofa dg/dt trend.
 9. The method of claim 8, further comprising the stepsof: (a) calculating an angle α that is a function of dg/dt; and (b)displaying the arrow so that it is angled from horizontal by the angleα,
 10. The method of claim 7, wherein the perceptible alarm comprises analarm selected from a list consisting of: a blinking display, a brightlycolored display, a vibratory alarm, an audible alarm, and combinationsthereof.
 11. The method of claim 7, wherein the display shows differentsmoothed CGM values as corresponding different colors.
 12. The method ofclaim 7, further comprising the step of displaying a trend box thatindicates a CGM data trend.